【摘要】：移動機(jī)器人集中了傳感器技術(shù)、機(jī)械、電子、計算機(jī)、自動化控制以及人工智能等多學(xué)科的研究成果,其發(fā)展是一個國家高科技水平和工業(yè)自動化程度的重要體現(xiàn)。在移動機(jī)器人相關(guān)的技術(shù)研究中,路徑規(guī)劃技術(shù)是機(jī)器人研究領(lǐng)域的重要組成部分,也是機(jī)器人完成指定任務(wù)的必要基礎(chǔ)和根本保障,其任務(wù)是在一個存在障礙物的有界工作空間內(nèi),依據(jù)相應(yīng)的評價標(biāo)準(zhǔn)(移動時間、路徑長度、能量消耗等)能夠根據(jù)周圍環(huán)境信息自主的從給定起點運(yùn)動到目標(biāo)點,同時確保機(jī)器人與障礙物之間、機(jī)器人與機(jī)器人之間不發(fā)生碰撞。至今,大部分研究都集中在靜態(tài)環(huán)境下單機(jī)器人路徑規(guī)劃,而多機(jī)器人系統(tǒng)能夠有效的處理復(fù)雜、動態(tài)性高以及需要并行完成的任務(wù),具有單機(jī)器人無法比擬的優(yōu)勢,因此研究動態(tài)以及多機(jī)器人環(huán)境下的路徑規(guī)劃問題具有重要意義。目前,現(xiàn)有的優(yōu)化算法在求解路徑規(guī)劃問題時都存在各自的缺陷,因此尋求更佳的算法就成為該領(lǐng)域的一個研究重點。鑒于遺傳算法具有較強(qiáng)的魯棒性、并行性以及較強(qiáng)的全局搜索能力等優(yōu)點,本文研究設(shè)計了改進(jìn)的遺傳算法,并將其應(yīng)用于單機(jī)器人以及多機(jī)器人路徑規(guī)劃中。論文的主要研究內(nèi)容如下:1.研究了全局環(huán)境下移動機(jī)器人路徑規(guī)劃問題,為了解決基本遺傳算法在求解機(jī)器人路徑規(guī)劃問題中存在的收斂速度慢、易陷入局部最優(yōu)等不足,對遺傳算法進(jìn)行了改進(jìn)。引入了人工勢場法進(jìn)行種群初始化,提出了一種基于種群多樣性程度評價的自適應(yīng)選擇方法,設(shè)計了自適應(yīng)交叉和變異概率,改善了基本遺傳算法收斂速度慢和早熟現(xiàn)象,提高了算法的求解質(zhì)量。在柵格環(huán)境下進(jìn)行多次仿真實驗,證明了所提出改進(jìn)遺傳算法的可行性和有效性。2.針對動態(tài)環(huán)境下單移動機(jī)器人路徑規(guī)劃問題,在規(guī)劃過程中將全局路徑規(guī)劃與局部路徑規(guī)劃相結(jié)合,并且根據(jù)機(jī)器人與動態(tài)障礙物碰撞類型的不同,制定了有效的避碰策略。仿真實驗表明該方法能夠有效的指導(dǎo)機(jī)器人在動態(tài)環(huán)境中實現(xiàn)避障,獲得無碰最優(yōu)或次優(yōu)路徑。3.針對動態(tài)環(huán)境下多機(jī)器人路徑規(guī)劃進(jìn)行了研究,為解決機(jī)器人間的路徑?jīng)_突問題,制定了有效的路徑協(xié)調(diào)策略,實驗結(jié)果表明該方法能夠較好地實現(xiàn)多機(jī)器人路徑規(guī)劃。
[Abstract]:The development of mobile robot, which includes sensor technology, machinery, electronics, computer, automation control and artificial intelligence, is an important embodiment of a country's high-tech level and industrial automation. In the research of mobile robot technology, path planning technology is an important part of robot research field, and it is also the necessary foundation and fundamental guarantee for robot to complete the assigned task. In a bounded workspace with obstacles, according to the corresponding evaluation criteria (moving time, path length, energy consumption, etc.), the task is to automatically move from the given starting point to the target point according to the information of the surrounding environment. At the same time, make sure that there is no collision between robot and obstacle and between robot and robot. Up to now, most of the researches have focused on the path planning of the robot in static environment, and the multi-robot system can deal with complex, dynamic and parallel tasks effectively, which has the advantage that the single robot can't compare with each other. Therefore, it is of great significance to study the dynamic and path planning problems in multi-robot environment. At present, the existing optimization algorithms have their own defects in solving the path planning problem, so the search for better algorithms has become a research focus in this field. In view of the strong robustness, parallelism and global search ability of genetic algorithm, an improved genetic algorithm is designed and applied to single robot and multi-robot path planning. The main contents of this thesis are as follows: 1. In this paper, the path planning problem of mobile robot in the global environment is studied. In order to solve the shortcomings of the basic genetic algorithm in solving the robot path planning problems, such as slow convergence speed and easy to fall into local optimum, the genetic algorithm is improved. The artificial potential field method is introduced to initialize the population, and an adaptive selection method based on the evaluation of population diversity is proposed. The adaptive crossover and mutation probability are designed to improve the slow convergence speed and premature convergence of the basic genetic algorithm. The quality of the algorithm is improved. Several simulation experiments in grid environment show that the proposed improved genetic algorithm is feasible and effective. For the path planning problem of mobile robot in dynamic environment, the global path planning and local path planning are combined in the planning process, and an effective collision avoidance strategy is proposed according to the different collision types between the robot and the dynamic obstacle. The simulation results show that this method can effectively guide the robot to avoid obstacles in dynamic environment and obtain the optimal or sub-optimal path. 3. In this paper, the path planning of multi-robot in dynamic environment is studied. In order to solve the problem of path conflict between robots, an effective path coordination strategy is proposed. The experimental results show that the method can achieve multi-robot path planning well.
【學(xué)位授予單位】：安徽工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 田欣;劉廣瑞;周文博;郭珂甫;;基于改進(jìn)自適應(yīng)遺傳算法的機(jī)器人路徑規(guī)劃研究[J];機(jī)床與液壓;2016年17期

2 劉曉磊;蔣林;金祖飛;郭晨;;非結(jié)構(gòu)化環(huán)境中基于柵格法環(huán)境建模的移動機(jī)器人路徑規(guī)劃[J];機(jī)床與液壓;2016年17期

3 梁嘉俊;曾碧;何元烈;;基于改進(jìn)勢場柵格法的清潔機(jī)器人路徑規(guī)劃算法研究[J];廣東工業(yè)大學(xué)學(xué)報;2016年04期

4 張毅;代恩燦;羅元;;基于改進(jìn)遺傳算法的移動機(jī)器人路徑規(guī)劃[J];計算機(jī)測量與控制;2016年01期

5 屈鴻;黃利偉;柯星;;動態(tài)環(huán)境下基于改進(jìn)蟻群算法的機(jī)器人路徑規(guī)劃研究[J];電子科技大學(xué)學(xué)報;2015年02期

6 史恩秀;陳敏敏;李俊;黃玉美;;基于蟻群算法的移動機(jī)器人全局路徑規(guī)劃方法研究[J];農(nóng)業(yè)機(jī)械學(xué)報;2014年06期

7 張慶龍;劉國棟;;一種基于改進(jìn)人工勢場的移動機(jī)器人路徑規(guī)劃方法[J];江南大學(xué)學(xué)報(自然科學(xué)版);2014年02期

8 黎萍;朱軍燕;彭芳;楊亮;;基于可視圖與A*算法的路徑規(guī)劃[J];計算機(jī)工程;2014年03期

9 藺婧娜;張立亞;;改進(jìn)的多傳感器融合的機(jī)器人局部路徑規(guī)劃[J];計算機(jī)測量與控制;2013年08期

10 譚民;王碩;;機(jī)器人技術(shù)研究進(jìn)展[J];自動化學(xué)報;2013年07期

相關(guān)博士學(xué)位論文 前3條

1 馬勇;多移動機(jī)器人路徑規(guī)劃研究[D];華中科技大學(xué);2012年

2 劉傳領(lǐng);基于勢場法和遺傳算法的機(jī)器人路徑規(guī)劃技術(shù)研究[D];南京理工大學(xué);2012年

3 劉淑華;復(fù)雜動態(tài)環(huán)境下多機(jī)器人的運(yùn)動協(xié)調(diào)研究[D];吉林大學(xué);2005年

相關(guān)碩士學(xué)位論文 前5條

1 趙媛;基于遺傳算法的移動機(jī)器人路徑規(guī)劃的研究[D];河南科技大學(xué);2014年

2 柯星;動態(tài)環(huán)境下多移動機(jī)器人路徑規(guī)劃研究[D];電子科技大學(xué);2013年

3 萬超;多機(jī)器人系統(tǒng)中的機(jī)器人路徑規(guī)劃研究[D];廣東工業(yè)大學(xué);2011年

4 浦定超;基于遺傳算法的移動機(jī)器人路徑規(guī)劃的研究[D];合肥工業(yè)大學(xué);2010年

5 李靜;多移動機(jī)器人路徑規(guī)劃避碰和協(xié)調(diào)研究[D];上海交通大學(xué);2007年

，

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